System for near optimal joint channel estimation and data detection for COFDM systems

ABSTRACT

Joint channel estimation and maximum likelihood decoding method for Coded Orthogonal Frequency Division Multiplexing (COFDM) systems are presented. Using this method in conjunction with convolutional coding, robust and nearly optimal coherent detection can be achieved in rapid dispersive fading channels. Significant performance gain in packet data throughput is realized in a system with aggressive frequency reuse. A system for estimating channel characteristics in a multicarrier transmission system comprising means for receiving a multicarrier signal, means for applying Fast Fourier transformations to the multicarrier signal, means for estimating channel characteristics of a multicarrier channel over which the multicarrier signal was transmitted using iterative processing and means for decoding the transformed multicarrier signal is presented.

This application claims the benefit of priority of ProvisionalApplication No. 60/241,295, filed on Oct. 19, 2000, and of ProvisionalApplication No. 60/180,799, filed on Feb. 7, 2000, and is related bysubject matter to U.S. patent application Ser. No. 09/774,875 filed Feb.1, 2001, entitled “Method for Near Optimal Joint Channel Estimation andData Detection for COFDM Systems,” by the inventors of the presentapplication filed concurrently herewith.

FIELD OF INVENTION

The invention relates generally to communications and particularly to amethod and apparatus for near optimal joint channel estimation and datadetection to improve channel tracking and, thus, improve linkrobustness.

BACKGROUND OF THE INVENTION

The rapid growth in the use of the Internet and the increasing interestin portable computing devices have triggered the desire for high-speedwireless data services. One of the more promising candidates forachieving high data rate transmission in a mobile environment isOrthogonal Frequency Division Multiplexing (OFDM), which divides thewide signal bandwidth into many narrow-band subchannels, which aretransmitted in parallel. Each subchannel is typically chosen narrowenough to eliminate the effects of delay spread. Coded OFDM (COFDM)systems, which combine both OFDM and channel coding techniques, are ableto improve the performance further by taking advantage of frequencydiversity of the channel.

Though both differential and coherent demodulation can be applied in aCOFDM system, the latter leads to a performance gain of 3 to 4-dB insignal-to-noise ratio (SNR) with accurate channel estimation. Channelestimation techniques realized by a frequency-domain filter using FastFourier Transform (FFT), followed by time-domain filters for a COFDMsystem with Reed-Solomon (RS) coding have been proposed. These channelestimation techniques, while good, did not provide the near optimalchannel estimation required for data-decoding with improved channeltracking capability for reliable link performance even under high usermobility and/or high RF carrier frequency.

SUMMARY OF THE INVENTION

The physical layer configuration is shown in FIG. 1A. At thetransmitter, the encoded data stream is sent to an OFDM transmissionbranch. The data stream may, for example, be convolutionally encoded.The encoded data stream may then be optionally interleaved. If theencoded data stream is interleaved in the transmitter then the receivermust correspondingly deinterleave the data stream. After interleaving,the transmitter modulates the encoded data stream. By way of example,QPSK modulation is used. The signal is then subjected to inverse FastFourier Transformation and transmitted, in the present invention overthe air.

Correspondingly, a receiver accepts multicarrier transmitted signals andsubjects these received signals to Fast Fourier Transformation. Thetransformed signals are concurrently fed into a channel estimator anddemodulators. The demodulated signals are combined in a maximum ratiocombiner, optionally deinterleaved and decoded.

An OFDM signal is divided into a number of subchannels. By way ofexample, an OFDM signal bandwidth is divided into 120 6.25-kHzsubchannels with QPSK modulation on each subchannel. At the receiver,the demodulated signals from two receiving branches are combined usingmaximal ratio combining and then decoded. With a symbol period of 200 μs(including a 40-μs guard interval) and ½-rate coding, a maximuminformation rate of 600 kbps can be achieved in a 750-kHz bandwidth(about 800 kHz including guard bands). The information rate iscalculated by dividing the 120 subchannels (tones) by the 200 μs periodto obtain 600 kbps.

For purposes of example for the present invention, ½-rate convolutionalcodes (CC) are considered. The results with ½-rate Reed-Solomon (RS)code based on Galois-Field (64) (GF(64)) are compared. The size of acode word is the same as that of an OFDM block (an OFDM symbol of 200 μsand 120 subchannels). To achieve coding gain with inherent frequencydiversity in OFDM, a simple interleaving scheme is applied. For both RSand CC cases, the first 120 bits of a code word are assigned to thein-phase component and the rest to the quadrature component. To gainadditional randomness within a code word for the CC case, each 120-bitgroup is interleaved over subchannels by an 11-by-11 block interleaver(without the last bit).

In the simulations, the wireless channel, as a Rayleigh-fading channel,with a two-ray multipath delay profile is modeled. Good performance forimpulse separation as high as 40-μs can be achieved; a 5-μs impulseseparation in the numerical results is considered.

For the performance with respect to channel variations, maximum Dopplerfrequency up to 200 Hz, which is reasonable for most vehicular speeds,for a possible RF carrier frequency around 2 GHz is considered. Todemonstrate the advantage of the proposed joint detection methods,results at a maximum Doppler frequency as high as 500 Hz correspondingto a scenario in which the wireless system uses a higher carrierfrequency, e.g. 5 GHz are presented.

In the medium access control (MAC) layer, a frequency reuse isconsidered with dynamic resource management, e.g., Dynamic PacketAssignment (DPA), to achieve high spectral efficiency for packet dataaccess.

A simple analysis to highlight the ideal or optimal joint channelestimation and maximum likelihood (ML) decoding scheme indicated in FIG.1A for the case of M=2 receiving antennas is now presented.

At a diversity receiver, the signal from the m th antenna at the k thsubchannel and the n th block can be expressed as

x _(m,n,k) =h _(m,n,k) a _(n,k) +w _(m,n,k),   (1)

where a_(n,k), h_(m,n,k) and w_(m,n,k) are the transmit signal, channelresponse and additive Gaussian noise, respectively.

For convolutional codes, because the size of a code word is the same asthat of the OFDM block, (1) can be rewritten as

x _(m,n) =H _(m,n) c _(n) +w _(m,n),   (2)

where, if there are K_(f) subchannels,

H _(m,n) =diag(h _(m,n,1) , h _(m,n,2) , . . . , h _(m,n,K) _(f) ),

c_(n) is the transmitted code word at time epoch n, and the rest of thevectors are similarly defined.

Assume that the number of code words is N, we introduce the followingnotations,

c=[c ₁ ^(T) , c ₂ ^(T) , . . . , c _(N) ^(T)]^(T),

H _(m) =diag(H _(m,1) , H _(m,2) , . . . , H _(m,N)),

x _(m) =[x _(m,1) ^(T) , x _(m,2) ^(T) , . . . , x _(m,N) ^(T)]^(T).  (3)

At the receiver, the objective is to solve a maximum likelihood (ML)problem $\begin{matrix}{{\hat{c} = {\underset{c}{\arg \quad \min}\left\lbrack {\min\limits_{H_{m}}{\sum\limits_{m}{{x_{M} - {H_{m}c}}}^{2}}} \right\rbrack}},} & (4)\end{matrix}$

with a constraint on channel response

L(H _(m))=0,   (5)

where L() is a constraint function. In a wireless environment, thisconstraint can be simplified to be $\begin{matrix}{{{\sum\limits_{l = {- K_{m}}}^{K_{m}}{B_{n,l}{d\left( H_{m,{n - l}} \right)}}} = 0},} & (6)\end{matrix}$

where the length of the channel memory is K_(m) OFDM symbol durations,B_(n,l) are coefficients determined by the correlation between channelresponses at the time epochs n and n−1, which is a function of theDoppler spectrum of the channel, and d() is a vector function defined by

d(H _(m,n))=[h _(m,n,1) , h _(m,n,2) , . . . , h _(m,n,K) _(f) ]^(T).

The optimal solution of this ML problem can be obtained by exhaustivesearch. It requires solving the mean square error (MSE) $\begin{matrix}{{{{MSE}(c)} = {\min\limits_{H_{m}}{\sum\limits_{m}{{x_{m} - {H_{m}c}}}^{2}}}},} & (7)\end{matrix}$

for any possible c with the channel constraint (6). Then,$\begin{matrix}{\hat{c} = {\underset{c}{\arg \quad \min}{{{MSE}(c)}.}}} & (8)\end{matrix}$

After obtaining MSE(c), the corresponding channel estimate H_(m)(c) canbe found. Consequently, the optimal approach for estimating channelresponse requires the knowledge of the entire set of x and c.

Another observation from this ML receiver is that the channel estimationresults H_(m) is not a direct output of the detection process and hence,channel estimation which calculates H_(m) explicitly may not benecessary in theory. However, for other required parameter estimation,such as timing and frequency synchronization, a known data sequence isusually transmitted in the beginning of a group of OFDM blocks. Thisknown data sequence, also called a synch word or a unique word, can beused as a training sequence in (7) to obtain initial channel estimateexplicitly without resorting to blind detection. This initial channelcharacteristic is helpful for solving this ML problem with betternumerical stability and tracking property. This initial channelestimation can be easily solved in the frequency domain by first takingFFT as shown in FIG. 1A.

One related method and system is Ser. No. 09/089,862, METHOD ANDAPPARATUS FOR CHANNEL ESTIMATION FOR MULTICARRIER SYSTEMS, which wasfiled Jun. 3, 1998, and is commonly held and incorporated herein byreference. The near optimal joint channel estimation and data detectionmethod and system of the present invention was born from the researchthat resulted in that application and the subsequent determination thatimprovements could be made in the channel estimation.

The sub-optimal approach of the related system and method is nowoutlined. Because of the formidable complexity of the optimal MLreceiver, some sub-optimal solutions are widely used in practice. Therelated sub-optimal solution is to divide the ML problem into two parts,channel estimation and coherent decoding. Then, the problem can besolved by iteratively estimating channel and decoding in the forwarddirection (in time).

At a time instant n, given a channel estimate Ĥ_(m,n), initiallyobtained by using training sequence (in the frequency domain dividingthe transfer function of the received signal by the transfer function ofthe known data), the maximum likelihood (ML) problem $\begin{matrix}{{\hat{c}}_{n} = {\underset{c_{n}}{\arg \quad \min}{\sum\limits_{m}{{x_{m,n} - {{\hat{H}}_{m,n}c_{n}}}}^{2}}}} & (9)\end{matrix}$

can be solved. Then, the reference for channel estimation is$\begin{matrix}{{\overset{\sim}{H}}_{m,n} = {\underset{H_{m,n}}{\arg \quad \min}{\sum\limits_{m}{{{x_{m,n} - {H_{m,n}{\hat{c}}_{n}}}}^{2}.}}}} & (10)\end{matrix}$

Finally, the estimate for the time instant n+1 is obtained by solving alinear constrained equation (6). Considering a stationary channel withfixed maximum Doppler frequency, the coefficients B_(n,l) areindependent of n, and can be written as B_(l), which are used as thecoefficients in the FIR filter to track channel variations.Consequently, a simplification of (6) with only previous references forprediction-type estimation leads to $\begin{matrix}{{{{\sum\limits_{l = 1}^{M_{L}}{B_{l}{d\left( {\overset{\sim}{H}}_{m,{n + 1 - l}} \right)}}} - {d\left( {\hat{H}}_{m,{n + 1}} \right)}} = 0},} & (11)\end{matrix}$

where M_(l) is the number of taps of an FIR filter and B_(l) are presetcoefficients designed to achieve the minimum mean square error (MMSE) ofestimation. This MMSE estimator can be realized by a frequency-domainfilter using the Fast Fourier Transform (FFT), followed by an M_(l)-taptime-domain filter,

B _(l) =b _(l) F ⁻¹ B _(f) F,   (12)

where b_(l) is the time-domain filter coefficient, F is the FFT matrix,B_(f) is a diagonal matrix, and F⁻¹B_(f)F is the frequency-domainfilter.

The MMSE filter coefficients b_(l) and B_(f) were derived assumingĉ_(n)=c_(n) for a given set of Doppler frequency and delay spread. It isshown that this estimator is robust regardless of frequency or timemismatches. With a low Doppler frequency, it has been shown, that a5-tap (M_(l)=5) estimator can successfully predict the channel.

To obtain accurate initial channel estimation, a training OFDM block issent at the beginning of a transmission, in which c₁ is known to obtain{tilde over (H)}_(m,1). The channel parameters for the new time epochsand the unknown code words can be successively solved in the forwarddirection (time advance).

The assumption ĉ_(n)=c_(n) cannot be always guaranteed; an incorrectlydetected code word introduces wrong channel estimation and hence, cancause a wrong detection of the successive code words. This errorpropagation is the dominant impairment of the link performance at highSNR. In order to alleviate the problem of error propagation, a trainingOFDM block is inserted every N_(t) block. In the simulations presentedherein, N_(t)=10 is considered.

In FIG. 2, the performance of the coherent reception with RS code andconvolutional codes for 40-Hz maximum Doppler frequency is shown. Theconvolutional codes are shown with different constraint length (K)ranging from 3 to 9 as dashed lines. The performance of convolutionalcodes is substantially better than that of the RS code, which is shownas a solid line with cross lines. In order to achieve a Word Error Rate(WER) of 10⁻², the K=9 CC needs 4 dB lower SNR than the RS code. In theuse of WER in the present invention, it is assumed that a word is acodeword. Moreover, the performance of the K=9 CC with channelestimation is very close to the one with the ideal channel informationshown as a solid line.

In FIG. 3, the performance at 200-Hz Doppler is shown. In comparisonwith the RS code, the CC's are still superior although the degradationwith respect to the idealized case is higher due to poorer channeltracking. In fact, an error floor at the high SNR region exists due totracking errors. Once again the RS coded signal is indicated as a solidline with cross lines. The convolutionally coded signals are indicatedby dashed lines and ideal channel information is indicated by a solidline.

In FIG. 4, the performance of the K=9 CC with different maximum Dopplerfrequencies is shown. With a low Doppler frequency, a 5-tap (M_(l)=5)estimator used here can successfully predict the channel and theperformance is very close to that with idealized channel estimation.However, when the fading is relatively fast, it is difficult to estimatethe channel correctly and the WER floors on the order of 10⁻³ can beclearly observed for at a maximum Doppler frequency of 200 Hz. That is,it was found that the original method works well in slow fading butdegrades significantly in fast fading. Once again the ideal Dopplerfrequency is indicated by a solid line. A Doppler frequency of 200 Hz isindicated by a dashed line. A Doppler frequency of 175 Hz is indicatedby a dashed line with small circles. A Doppler frequency of 150 Hz isindicated by a dashed line with triangles and a Doppler frequency of 125Hz is indicated by a dashed line with cross lines.

FIG. 1B depicts a block diagram for a baseband receiver. It issimplified to the extent that only channel estimator and decoderportions of the receiver are depicted. The decoder further comprises aplurality of demodulators, as well as a maximum ratio combiner, anoptional deinterleaver (to match an optional interleaver in thetransmitter) and a Viterbi decoder, all depicted in FIG. 1A. However,FIG. 1B depicts the structure and corresponding connections of thereceiver depicted in FIG. 1A. Note, all x, ĉ, Ĥ, {tilde over (H)} arecomplex. Although it may seem strange to have a complex codeword, ĉ, itis very natural to treat coding and modulation as a whole likespatial-temporal coding as discussed in “Space-time codes for high datarate wireless communication: performance criterion and codeconstruction”, by V. Tarokh, N. Seshadri, and A. Calderbank, publishedin IEEE Trans. Info. Theory, vol. 44, no. 2, pp. 744-765, March 1998 orcoded modulation “Channel coding with multilevel/phase signals,” by G.Ungerboeck, published in IEEE Trans. Info. Theory, vol. IT-28, no. 1,pp. 55-67, January 1982.

At a time instant n, the channel estimator unit has two tasks. One is toproduce the channel estimates of current time instant, Ĥ_(1,n), Ĥ_(2,n),. . . with its input x_(1,n), x_(2,n), . . . and feedback {tilde over(H)}_(1,,n−M) _(l) , {tilde over (H)}_(2,n−M) _(l) , . . . , {tilde over(H)}_(l,n−1), {tilde over (H)}_(2,n−1),, by equation (11). The othertask is to produce references {tilde over (H)}_(1,n), {tilde over(H)}_(2,n), . . . for estimate processing in the next time instant byequation (10) when ĉ_(n) is available. At a time instant n, whenĤ_(1,n), Ĥ_(2,n), . . . are available, the decoder unit produces decodedinformation ĉ_(n) by (9).

The flowchart of this method is shown in FIG. 1c. The related channelestimation method is first initialized at step 165-1. Transmittedsignals are received at step 165-2. A determination is then made at step165-3 as to whether the received block is a training block. If thereceived block is a training block then ĉ_(n) is known and${\overset{\sim}{H}}_{m,n} = {\underset{H_{m,n}}{\arg \quad \min}{\sum\limits_{m}{{x_{m,n} - {H_{m,n}{\hat{c}}_{n}}}}^{2}}}$

is calculated at step 165-5. This is a reference for the channelestimation${{\sum\limits_{l = 1}^{M_{L}}{B_{l}{d\left( {\overset{\sim}{H}}_{m,{n + 1 - l}} \right)}}} - {d\left( {\hat{H}}_{m,{n + 1}} \right)}} = 0$

which is calculated next at step 165-6. The block number is incrementedat step 165-7 and a determination is made if the end of the frame hasbeen reached at step 165-8. If the current block is not a training blockthen ĉ_(n) is decoded at step 165-4${\hat{c}}_{n} = {\underset{c_{n}}{\arg \quad \min}{\sum\limits_{m}{{x_{m,n} - {{\hat{H}}_{m,n}c_{n}}}}^{2}}}$

is calculated before calculating the reference and channel estimation.

It should be clear from the foregoing that there is room for improvementbetween the prior art and optimal joint channel estimation and datadetection. An object, therefore, of the present invention is to improvethe joint channel estimation. This will have the effect of reducing theimpact of noise as well as reducing decoding errors. Thus, overallsystem performance will be improved.

It is a further object of the present invention to provide a method andsystem that are robust even in light of a mismatch between FiniteImpulse Response (FIR) coefficients and the true channel.

Another object of the present invention is to provide a method andsystem with improved channel tracking capability, resulting in reliablelink performance even under high user mobility and/or high RF carrierfrequency. With improved link performance, data rates significantlyhigher than currently available (or even than third generation systemsin planning) can be offered to subscribers.

All of the above objects can be achieved nearly optimally even in rapiddispersive fading channels.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will now be described in greater detail withreference to the preferred embodiments of the invention, given only byway of example, and illustrated in the accompanying drawings, in whichsame elements are numbered the same to the extent possible:

FIG. 1A is the physical configuration of a system for transmitting andreceiving signals.

FIG. 1B is a block diagram for a baseband receiver at time instant n.

FIG. 1C is a flowchart for a related sub-optimal channel estimationmethod.

FIG. 2 is a comparison of RS and convolutional codes, where the maximumDoppler frequency is 40 Hz.

FIG. 3 is a comparison of RS and convolutional codes, where the maximumDoppler frequency is 200 Hz.

FIG. 4 depicts the performance of the K=9 CC at different maximumDoppler frequencies.

FIG. 5 depicts the performance of the iterative approach at 200 Hzmaximum Doppler frequencies, where K=9.

FIG. 6A is a flowchart for the iterative processing.

FIG. 6B is a flowchart for the iterative backward processing.

FIG. 7 shows the performance of iterative backward-processing approachat 200 Hz maximum Doppler frequency, where K=9.

FIG. 8 shows the performance of iterative backward-processing approachat different maximum Doppler frequencies, where K=9.

FIG. 9 is a comparison of systems having different time-domain FIRestimators, where K=9.

FIG. 10 shows the average retransmission probability as a function ofoccupancy per sector. Eight slots and three RF carriers (<2.5 MHz) arereused in every base station, each with three sectors, using DynamicPacket Assignment (DPA). In the original (non-iterative) method, K=5, 40Hz Doppler or K=9, 125 Hz Doppler.

FIG. 11 shows the average delay of delivered packets as a function ofthroughput per base station, each with three sectors. Eight slots andthree RF carriers (<2.5 MHz) are reused in everywhere using DPA. In theoriginal (non-iterative) method, K=5, 40 Hz Doppler or K=9, 125 HzDoppler.

FIG. 12 depicts the average retransmission probability as a function ofoccupancy per sector. Eight slots and three RF carriers (<2.5 MHz) arereused in every base station, each with three sectors, using DPA, whereK=9, 200 Hz Doppler.

FIG. 13 shows the average delay of delivered packets as a function ofthroughput per base station, each with three sectors. Eight slots andthree RF carriers (<2.5 MHz) are reused in everywhere using DPA, whereK=9, 200 Hz Doppler.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

A method and system using two channel estimation schemes, which improvechannel-tracking capability based on simplification of the ideal oroptimal detector are now presented.

The physical layer configuration of the system for near optimal jointchannel estimation and data detection for COFDM systems is depicted inFIG. 1A. An exemplary transmitter 105 is shown on top and an exemplaryreceiver 140 is shown on the bottom of FIG. 1A. At transmitter 105, adata stream is accepted by a Convolutional encoder 110, whichconvolutionally encodes the data stream. The encoded data stream maythen optionally be forwarded to an interleaver 115 for interleaving. Ifthe encoded data stream is interleaved in the transmitter 105 then thereceiver 140 must correspondingly de-interleave the encoded data stream.After interleaving. a modulator 120, for example a QPSK modulator,modulates the encoded (and optionally interleaved) data stream, which isthen forwarded to an inverse Fast Fourier transformer 125, to subjectthe modulated encoded (and optionally interleaved) data stream toinverse Fast Fourier transformation. The transformed modulated (andoptionally interleaved) encoded data stream (signal) is thentransmitted, in the present invention, over the air via RF unit 130 andantenna 135.

Correspondingly, receiver 140 accepts multicarrier transmitted signals(data streams) via antennas 145 and RF units 150 and subjects thereceived multicarrier signals to Fast Fourier transformation using FastFourier transformers 155. These transformed signals are concurrently fedinto channel estimator 165 and demodulators 160, for example QPSKdemodulators. The demodulated transformed signals are combined inmaximum ratio combiner 170. The combined demodulated transformed arethen optionally de interleaved using de-interleaver 175. The combineddemodulated transformed (and optionally de interleaved) signal is thendecoded using Viterbi decoder 180. The decoded combined demodulatedtransformed (and optionally de-interleaved) signal is then fed back intochannel estimator 165, which forwards channel estimations, which areadded to the transformed signals that are forwarded to demodulators 160.

FIG. 1B shows the baseband processing, in particular, the iterativenature of the receiver portion of the system for near optimal jointchannel estimation and data detection for COFDM systems. Channelestimator 165 accepts transformed signal 190. Channel estimations 198are fed back into channel estimator 165. Channel estimations 194 are fedinto decoder 185, which comprises maximum ratio combiner 170 (shown inFIG. 1A), optional de-interleaver 175 (shown in FIG. 1A) and Viterbidecoder 180 (shown in FIG. 1A). Channel estimations 195 are fed intodecoder 185 via demodulator 160 (shown in FIG. 1A, but not shown in FIG.1B for clarity and to highlight the iterative nature of the system),which demodulates the transformed signal using channel characteristics.Decoder 185 also accepts transformed signal 190. Decoder 185 outputssignal 192, which is fed back into channel estimator 165.

The present invention uses a COFDM system with convolutional coding(CC). Moreover, the present invention uses two iterative processingtechniques using current and future tentative data decisions in thebackward direction for channel estimation and the final decoding. Thispermits joint channel estimation and data decoding with improvedchannel-tracking capability, resulting in reliable link performance evenunder high user mobility and/or high RF carrier frequency. System levelperformance such as retransmission probability and packet delay in asystem with aggressive frequency reuse using dynamic packet assignment(DPA) is also presented.

Since the wireless channel is correlated in time, the optimal jointdetection requires processing the received signals and the decoded datain the past, current and future epochs. From (10) and (11), it can beseen that the joint detector in the related is sub-optimal because itattempts to predict the channel response at a time instant by only usingreceived signals and decoded information in the past. In order toimprove channel estimation by applying the current information, e.g.decoded data and newly received signal, an iterative solution can beapplied.

At each time epoch n, after performing (9), (10) and (11), the presentinvention does not proceed to the time epoch n+1 immediately. Instead,the present invention uses the newly generated Ĥ_(m,n,+1), which isderived based on the current information, to substitute for the currentchannel estimate Ĥ_(m,n) and the computations in (9), (10) and (11) arerepeated. In other words, the same procedures are performed twice. Thisadditional iteration allows channel estimation to be performed based onthe current information.

In FIG. 5, the improvement of the system performance is shown. At eachtime instant n, if the perfect past channel were known, i.e., [{tildeover (H)}_(m,1), , , {tilde over (H)}_(m,n−1)]=[H_(m,1), , , H_(m,n−1)]in (11), the new iterative processing can perform within 0.3 dB from thecase in which channel is known. On the other hand, even if the perfectpast channel information was available, the system performance wouldstill be far from the optimal one with an irreducible error floor in therelated sub-optimal solution. The SNR requirement of the new iterativeapproach at a WER of 10⁻² is 1.2 dB lower than that of the original one.System performance for the original signal is indicated by a dashedline. Using an iterative approach is indicated by a dashed line withtriangles. Using the original approach and having perfect past channelinformation is indicated by a dashed line with circles. Using aniterative approach and having perfect past channel information isindicated by a dashed line with cross lines. The ideal estimate isindicated by a solid line.

More than two iterations were attempted but the improvement was limited.Therefore, it was determined that another possible improvement probablyshould come from utilizing future information as described next.

Following the insights of the optimal approach, by taking into accountboth the current and the future information another step is performed.To take advantage of the future information in the channel estimationprocess, the FIR filter shown in (11) can be processed in thetime-reversed fashion, i.e., $\begin{matrix}{{{\sum\limits_{l = 1}^{M_{L}}{B_{l}{d\left( {\overset{\sim}{H}}_{m,{n + l - 1}} \right)}}} - {d\left( {\hat{H}}_{m,{n - 1}} \right)}} = 0.} & (13)\end{matrix}$

Hence, the iterative approach is performed backwards. Here, assume thatthe iterative approach mentioned in the last sub-section has beenalready carried out from time instant 1 to N. Therefore, x_(m) and ĉ areavailable with which to perform the iterative approach backward at alater time for tentatively detected previous signals; this method isthus referred to as iterative backward-processing approach. In order toprocess backward, {tilde over (H)}_(m,n) is stored when the decodingresult is correct (assuming an error detection mechanism is available)and ĉ and x_(m) when a decoding error occurs at a time instant n.

In general, there are three choices to perform the backward processing.The first choice is to process backward after all N OFDM-block data areprocessed. In reality, it is not a good choice because the channelmemory length K_(m) is not infinite and the delay of this approach maynot be acceptable if N is large. A feasible approach is to processbackward after having M_(l) (FIR tap length) consecutive correct codewords (OFDM blocks). This ensures that the backward processing willstart from a reliable channel estimate. The last choice is to processbackward starting at any other points, which is determined by memory anddelay requirements. Performance degradation is expected. However, if thebackward processing is started from a known training OFDM block, lowerdegradation can be achieved. With the last two choices, it is onlynecessary to store M_(N) (<<N in general) OFDM-block data in the memory.

The iterative processing and iterative backward processing approachesshare the similar baseband processing block diagram (FIG. 1A) with therelated/original approach. The only difference is that the feedback is{tilde over (H)}_(1,n−M) _(l) ₊₁, {tilde over (H)}_(2,n−M) _(l) ₊₁, . .. , {tilde over (H)}_(1,n), {tilde over (H)}_(2,n).. and {tilde over(H)}_(1,n−M) _(l) , {tilde over (H)}_(2,n−M) _(l) , . . . , {tilde over(H)}_(1,n+1), {tilde over (H)}_(2,n+1 . . .), respectively, whenperforming iterative processing and iterative backward processing. Theircorresponding flowcharts are shown in FIG. 6A and FIG. 6B.

The iterative channel estimation method depicted in FIG. 6A is firstinitialized at step 605. The iterative processing for estimating channelcharacteristics is performed by using the system depicted in FIGS. 1Aand 1B and as described above. Transmitted signals are received at step610. A determination is then made as to whether the received block is atraining block at step 615. If the received block is a training blockthen ĉ_(n) is known and${\overset{\sim}{H}}_{m,n} = {\underset{H_{m,n}}{\arg \quad \min}{\sum\limits_{m}{{x_{m,n} - {H_{m,n}{\hat{c}}_{n}}}}^{2}}}$

is calculated, which is a tentative reference signal, by firsttentatively decoding the block of the received multicarrier signal atstep 620.

The tentative reference signal is then used to generate a tentativeestimation for the channel at step 625 given by egn.${{\sum\limits_{l = 1}^{M_{L}}{B_{l}{d\left( {\overset{\sim}{H}}_{m,{n + 1 - l}} \right)}}} - {d\left( {\hat{H}}_{m,{n + 1}} \right)}} = 0$

The tentative reference signal is then used to generate a tentativeestimation for the channel at step 625 given by at step 635. The blocknumber is incremented at step 630 and a determination is made if the endof the frame has been reached at step 625. If the end of the frame hasnot been reached then another block of the received multicarrier signalis accepted for processing at step 610. If the current block is not atraining block then ĉ_(n)${\hat{c}}_{n} = {\underset{c_{n}}{\arg \quad \min}{\sum\limits_{m}{{x_{m,n} - {{\hat{H}}_{m,n}c_{n}}}}^{2}}}$

is calculated, which is a reference signal, by first decoding the blockof the received multicarrier signal at step 640. This reference given by${\overset{\sim}{H}}_{m,n} = {\underset{H_{m,n}}{\arg \quad \min}{\sum\limits_{m}{{x_{m,n} - {H_{m,n}{\hat{c}}_{n}}}}^{2}}}$

is calculated at step 645, which is then used to generate an estimationof channel characteristics given by at step 650.${{\sum\limits_{l = 1}^{M_{L}}{B_{l}{d\left( {\overset{\sim}{H}}_{m,{n + 1 - l}} \right)}}} - {d\left( {\hat{H}}_{m,{n + 1}} \right)}} = 0$

The block of the received multicarrier signal is then re-decoded usingthe estimation of the channel characteristics at step 655. The methodthen proceeds to the step after determining that the current block is atraining block. This effectively repeats the reference and channelestimation. The reference and channel estimation is repeated in order toimprove the calculations with the tentative reference and channelestimation calculations.

The iterative backward channel estimation method depicted in FIG. 6B isfirst initialized at step 660. The iterative backward processing forestimating channel characteristics is performed by and using the systemdepicted in FIGS. 1A and 1B and as described above. Transmitted signalsare received at step 665. A determination is then made as to whether thereceived block is correct at step 670. If the received block is correctthen ĉ_(n) is known and${\overset{\sim}{H}}_{m,n} = {\underset{H_{m,n}}{\arg \quad \min}{\sum\limits_{m}{{x_{m,n} - {H_{m,n}{\hat{c}}_{n}}}}^{2}}}$

is calculated, which is a tentative reference signal, by firsttentatively decoding the block of the received multicarrier signal atstep 675. This tentative reference signal is then used to generate atentative estimation for the channel at step 680 given by.${{\sum\limits_{l = 1}^{M_{L}}{B_{l}{d\left( {\overset{\sim}{H}}_{m,{n + 1 - l}} \right)}}} - {d\left( {\hat{H}}_{m,{n - 1}} \right)}} = 0.$

The block number is decremented at step 685 and a determination is madeif the beginning of the frame has been reached at step 690. If thebeginning of the frame has not been reached then another block of thereceived multicarrier signal is accepted for processing at step 665. Ifthe current block is correct block then ĉ_(n)${\hat{c}}_{n} = {\underset{c_{n}}{\arg \quad \min}{\sum\limits_{m}{{x_{m,n} - {{\hat{H}}_{m,n}c_{n}}}}^{2}}}$

is calculated, which is a reference signal, by first decoding the blockof the received multicarrier signal at step 692. This reference given by${\overset{\sim}{H}}_{m,n} = {\underset{H_{m,n}}{\arg \quad \min}{\sum\limits_{m}{{x_{m,n} - {H_{m,n}{\hat{c}}_{n}}}}^{2}}}$

at step 694 is then used to generate an estimation of channelcharacteristics at step 696 given by${{\sum\limits_{l = 1}^{M_{L}}{B_{l}{d\left( {\overset{\sim}{H}}_{m,{n + 1 - l}} \right)}}} - {d\left( {\hat{H}}_{m,{n - 1}} \right)}} = 0.$

(The block of the received multicarrier signal is then re-decoded usingthe estimation of the channel characteristics at step 698). The methodthen proceeds to the step after determining that the current block iscorrect. This effectively repeats the reference and channel estimation.The reference and channel estimation are repeated in order to improvethe calculations with the tentative reference and channel estimationcalculations.

In FIG. 7, the performance of this approach (iterative backwardprocessing) is shown for 200 Hz maximum Doppler frequency. In thesimulation, the maximum M_(N) is set to be 200 and this corresponds to40 ms. However, due to the low error probability in the high SNR region,much shorter storage is required. For instance, the maximum M_(N)required is about 50 at the 5 dB SNR. It is found that nearly optimalperformance is achieved with iterative backward processing. Systemperformance using the approach is indicated by a dashed line. Systemperformance using the iterative approach is indicated by a dashed linewith triangles. System performance using the iterative backwardprocessing approach of FIG. 6B is indicated by a dashed line with crosslines. The ideal estimate is indicated by a solid line.

In FIG. 8, the performance of this approach with different maximumDoppler frequencies is shown. The system still performs well in anenvironment with maximum Doppler frequency as high as 400 Hz. Once againthe ideal estimate is indicated by a solid line. System performance at500 Hz using iterative backward processing is indicted by a dashed linewith crosses (or “x”s). System performance at 400 Hz using iterativebackward processing is indicated by a dashed line with small circles.System performance at 300 Hz using iterative backward processing isindicated by a dashed line with triangles. System performance at 200 Hzusing iterative backward processing is indicated by a dashed line withcross lines. System performance of the original approach at 200 Hz isindicated by a dashed line.

By applying the simple iterative estimation, the impact of noise isreduced by an additional round of filtering with newly availableinformation. However, it may not eliminate the impact of decodingerrors. Using iterative backward-processing, there is a chance tore-estimate the channel with fewer decoding errors and hence, betterperformance is achieved.

It should be noted that the FIR estimator coefficients B_(l) in therelated sub-optimal method were optimized for the scenario without anyiterative process. It should be noted, however, the detectorarchitecture introduced here can be combined with any filter design forbetter tracking.

To show the robustness of the iterative approaches, a simple averaging5-tap finite impulse response FIR filter (b_(l)≡0.2) for the time domainfiltering is now considered. As shown in FIG. 9, even with these simpleFIR coefficients, the method of the present invention still outperformsthe original method with the FIR that was optimized for a particular setof maximum Doppler frequency and delay spread. Therefore, the iterativebackward-processing approach of the present invention is relativelyrobust against the mismatch between the FIR coefficients and the truechannel. The ideal estimate is indicated by a solid line. Systemperformance using the original approach and an optimal 5-tap FIR at 200Hz is indicated by a dashed and dotted line. System performance usingthe original approach and an optimal 5-tap FIR at 40 Hz is indicated bya dashed line. System performance using iterative backward processingand an optimal 5-tap FIR at 200 Hz is indicated by a dashed line withcrosses (or “x”s). System performance using iterative backwardprocessing and an optimal 5-tap FIR at 40 Hz is indicated by a solidline with crosses (or “x”s).

Consider a MAC layer configuration to characterize the system-levelperformance under frequency reuse using the improved detection methods.A simulation system of 36 base stations arranged in a hexagonal patternis used, each having 3 sectors, with a 20-dB front-to-back ratio andidealized antenna pattern. The same channel can be used everywhere, evenin different sectors of the same base station, as long as the Symbol toInterference Ratio (SIR) in the DPA admission process exceeds 7 dB. Thesimulation of the simultaneous use of the same spectrum by users indifferent cells results in interference between cells. Interference istreated as if it behaves like noise.

A channel is defined to be a combination of time slot and RF carrier,each consisting of 120 subchannels described previously. By way ofexample, the results for the case with 3 RF carriers and 8 time slotsare now presented. This occupies a total spectrum of less than 2.5 MHz,including guard bands and other overhead. Each time slot consists of 10OFDM blocks, i.e., 2 msec. One of these OFDM blocks is used fortraining, as discussed previously, while an additional block can beallocated for guard time between time slots. In addition, assume that acontrol slot of duration 4 msec is inserted in the beginning of everyframe of 8 traffic slots to enable paging, assignment and pilottransmission that are required for the DPA process. With thisconservative assumption of overhead in time and frequency domains, 48kb/s (8 OFDM blocks or 960 data bits transmitted in 20 msec) can bedelivered using each time slots. Once paged, a mobile station (MS)measures the pilot signals to determine the desired traffic slots andreports the list back to the base station (BS). The BS then assignstraffic channel(s) and informs the MS this assignment for traffic packetdelivery.

Based on the downlink frame structure, four adjacent BS's form a reusegroup and they take turns performing the DPA procedure once every 4frames. The time-reuse groups in the entire service area are pre-plannedin a fixed and repeated pattern.

For the propagation model, the average received power decreases withdistance d as d⁻⁴ and the large-scale shadow-fading distribution islog-normal with a standard deviation of 10 dB. A data-service trafficmodel, based on wide-area network traffic statistics, which exhibit a“self-similar” property when aggregating multiple sources, was used togenerate packets.

Automatic Retransmission reQuest (ARQ) is employed for retransmissionwhen a packet is received in error. A packet in this case istheoretically 8 code words in each time slot, but the error probabilityis represented by using the WER curves. Since the error probability ofthe 8 code words in a time slot is highly correlated and additionalcoding is usually included for the entire packet, this approximationprovides reasonable performance estimation for the MAC layer. If apacket cannot be successfully delivered in 3 seconds, which may be aresult of traffic overload or excessive interference, it is dropped fromthe queue. The control messages are assumed to be error-free in thedesignated control slots.

First consider K=5 and 40 Hz or K=9 and 125 Hz based on therelated/original (non-iterative) method, both give similar linkperformance (see FIGS. 2 and 5), for a comparison between RS and CCcoding methods. FIG. 10 shows the average probability of packetretransmission, as a function of occupancy for all available (24)channels in each sector. This is a measure of QoS (quality of service)experienced by individual users. With a 3-6% target retransmissionprobability, 15-50% occupancy per radio in each sector is possible withthis DPA scheme, depending on the use of coding schemes. Clearly, jointchannel estimation and maximum likelihood detection of CC indicated by adashed line introduced previously provides significant improvement overthe case of RS codes, indicated by a solid line with cross lines whichis also similar to the case of differential demodulation of the RS codeswith 4 transmit antennas. Both results are significantly superior to theefficiency provided by current cellular systems, which are typicallydesigned for voice communications with very conservative frequencyreuse, about 4-7% spectrum occupancy in each sector. Data applications,permitting some retransmission delay, and improved link design,introduced here, allow much more aggressive and efficiency frequencyreuse.

FIG. 11 shows that 1-1.5 Mb/s can be successfully delivered by each basestation with an average delay on the order of 40-120 msec. This is ameasure for system capacity. It indicates that OFDM link and DPA MACcombined enable a spectrally efficient (40%-60% b/s/Hz with aconservative assumption of overhead requirements) air interface forbroadband services, even for the macrocellular environment consideredhere. Adaptive modulation has not been considered in this study, and itsuse is expected to improve efficiency beyond 1 b/s/Hz per base stationeven under aggressive frequency reuse. The OFDM technology discussedherein can provide robust performance with peak-rates scalable with theavailable bandwidth. RS codes are indicated by a solid line with crosslines. CC codes are indicated by a dashed line.

Next, consider the case of high maximum Doppler frequency (200 kHz) andK=9 (WER curves in FIG. 6) for comparison between sub-optimal(“original”) detection method discussed earlier and near-optimaliterative backward-processing method presented. FIG. 12 shows thatretransmission probability using the improved method can work well evenunder high maximum Doppler frequency. As a result, QoS can be improvedeven for high mobility users or when higher carrier frequency isemployed. System performance using the original detection method isindicated by a solid line with cross lines. System performance using theiterative backward processing approach is indicated by a dashed line. Onthe other hand, the capacity difference is relatively smaller, as shownby the delay-throughput curves in FIG. 13. System performance using theoriginal approach is indicated by a solid line with cross lines andsystem performance using the iterative backward processing is indicatedby a dashed line. This is because both methods give very good radio linkperformance and the delay is dominated by sharing limited number oftraffic slots, which is independent of the WER performance. In thiscase, better traffic resource management, such as improved admissioncontrol, could achieve capacity improvement. If higher Dopplerfrequency, e.g., 400 Hz, were encountered, the improved link performanceintroduced by the iterative backward-processing method, as shown in FIG.8, would also result in system capacity enhancement.

Returning to FIG. 1A to show the detail of the decoder (DEC) unit andits relation with the Channel Estimator (CE) unit, with channelestimates Ĥ_(m,n), ĉ_(n) can be calculated by maximum likelihood (ML)decoding (9),${\hat{c}}_{n} = {\underset{c_{n}}{\arg \quad \min}{\sum\limits_{m}{{{x_{m,n} - {{\hat{H}}_{m,n}c_{n}}}}^{2}.}}}$

In FIG. 1A, this decoding process is divided into 4 parts, e.g. QPSKDemodulator, a maximum ratio combiner (MRC), Deinterleaver and Viterbidecoder. How to separate these 4 units is now explained.

We have $\begin{matrix}{{\sum\limits_{m}{{X_{m,n} - {{\hat{H}}_{m,n}c_{n}}}}^{2}} = {\sum\limits_{k}\left( {\sum\limits_{m}{{x_{m,n,k} - {h_{m,n,k}c_{n,k}}}}^{2}} \right)}} \\{= {\sum\limits_{k}{\left( {{\sum\limits_{m}{x_{m,n,k}}^{2}} - {2{Re}\left\{ {x_{m,n,k}^{*}h_{m,n,k}c_{n,k}} \right\}} + {{h_{m,n,k}c_{n,k}}}^{2}} \right).}}}\end{matrix}$

Note that $\sum\limits_{k}{\sum\limits_{m}{x_{m,n,k}}^{2}}$

makes no contribution to our minimization and due to binaryconvolutional code with QPSK modulation, ||c_(n,k)||² is a constant.$\sum\limits_{k}\left( {\sum\limits_{m}{{h_{m,n,k}}^{2}{c_{n,k}}^{2}}} \right)$

makes no contribution to our minimization either. So we concentrate on${\sum\limits_{k}\left( {\sum\limits_{m}{{Re}\left\{ {x_{m,n,k}^{*}h_{m,n,k}c_{n,k}} \right\}}} \right)} = {{\sum\limits_{k}{{Re}\left\{ {\left( {\sum\limits_{m}{x_{m,n,k}^{*}h_{m,n,k}}} \right)c_{n,k}} \right\}}} = {\sum\limits_{k}\left( {{\left\lbrack {\sum\limits_{m}{{Re}\left\{ {x_{m,n,k}^{*}h_{m,n,k}} \right\}}} \right\rbrack {Re}\left\{ c_{n,k} \right\}} - {\left\lbrack {\sum\limits_{m}{{Im}\left\{ {x_{m,n,k}^{*}h_{m,n,k}} \right\}}} \right\rbrack {Im}\left\{ c_{n,k} \right\}}} \right)}}$

The demodulator and the MRC calculate$\sum\limits_{m}{{Re}\left\{ {x_{m,n,k}^{*}h_{m,n,k}} \right\} \quad {and}\quad {\sum\limits_{m}{{Im}{\left\{ {x_{m,n,k}^{*}h_{m,n,k}} \right\}.}}}}$

And the remainder is done by the deinterleaver and the decoder.

Conceptually, the MRC does only energy combining. So, we perform thefollowing calculations:${\sum\limits_{m}{{Re}\left\{ {x_{m,n,k}^{*}h_{m,n,k}} \right\}}} = {\sum\limits_{m}{{Re}\left\{ {x_{m,n,k}^{*}{\arg \left( h_{m,n,k} \right)}} \right\} {h_{m,n,k}}}}$${\sum\limits_{m}{{Im}\left\{ {x_{m,n,k}^{*}h_{m,n,k}} \right\}}} = {\sum\limits_{m}{{Im}\left\{ {x_{m,n,k}^{*}{\arg \left( h_{m,n,k} \right)}} \right\} {{h_{m,n,k}}.}}}$

Then, Re{x^(*) _(m,n,k) arg(h_(m,n,k))} and Im{x^(*) _(m,n,k)arg(h_(m,n,k))} are actually QPSK demodulation. And the remainder is theMRC.

The radio-link performance of our COFDM system with the novel andnon-obvious combination of two channel estimation schemes under fastfading validate the design methodology and system for near optimal jointchannel estimation and data detection.

For a COFDM system, coherent detection can significantly improve radiolink performance, but it requires a channel estimator to achieve thepotential gain. The present invention applies a forward channelestimation and maximum likelihood decoding method for a COFDM systemwith convolutional codes and a simple interleaving scheme. It is shownthat the performance of such a system significantly outperforms the onewith Reed-Solomon codes. The performance under different fading rateswas studied to understand the performance limits and areas forimprovement. Simulations showed that this detection method is able toachieve near optimal performance in a wireless environment with maximumDoppler frequency as high as 100 Hz. However, as fading rate increasesfurther, irreducible error floor is introduced by the limitation inchannel-tracking capability. A near-optimal detection method has beenpresented to improve the channel tracking performance, which is based oniterative processing and iterative backward processing of channelestimation and data decoding. Within practical values of interest, errorfloor can be eliminated even for very high fading rates, which isimportant for cases with high mobility or when higher carrierfrequencies are considered in the future. Radio system performance basedon the improved link techniques and a medium access control protocolusing dynamic packet assignment was also studied. With radio linkimprovement, system throughput and delay can be significantly enhancedfor better quality of service and spectrum efficiency. The system isalso greatly simplified by allowing reuse factor of one withoutfrequency planning.

The present invention may be implemented in hardware, software orfirmware as well as Application Specific Integrated Circuits (ASICs) orField Programmable Gate Arrays (FPGAs) or any other means by which thefunctions and process disclosed herein can be effectively andefficiently accomplished or any combination thereof. The above means forimplementation should not be taken to be exhaustive but merely exemplaryand therefore, not limit the means by which the present invention may bepracticed.

It should be clear from the foregoing that the objectives of theinvention have been met. While particular embodiments of the presentinvention have been described and illustrated, it should be noted thatthe invention is not limited thereto since modifications may be made bypersons skilled in the art. The present application contemplates any andall modifications that fall within the spirit and scope of theunderlying invention disclosed and claimed herein.

What we claim is:
 1. A system for estimating channel characteristics ina multicarrier transmission system comprising: means for receiving amulticarrier signal; means for applying Fast Fourier transformations ofsaid received multicarrier signal; means for estimating channelcharacteristics of a multicarrier channel over which said multicarriersignal was transmitted using iterative processing; means for decodingsaid transformed multicarrier signal, wherein said means for decodingsaid transformed multicarrier signal further comprises means fordemodulating said multicarrier received signal; means for combining saiddemodulated multicarrier signal using a maximum ratio combiner and; andmeans for Viterbi decoding said combined signal.
 2. The system accordingto claim 1, wherein said iterative forward processing further comprises:means for iteratively determining if a block in a frame in the receivedmulticarrier signal is a training block; means for iteratively decodingsaid block of said received multicarrier signal tentatively, if saidblock of said received multicarrier signal is said training block; meansfor iteratively calculating a tentative reference signal based on saidtraining block, if said block of said received multicarrier signal issaid training block; means for iteratively generating a tentativeestimation of channel characteristics using said tentative referencesignal if said block of said received multicarrier signal is saidtraining block; means for iteratively decoding said block of saidreceived multicarrier signal, if said block of said receivedmulticarrier signal is not said training block; means for iterativelycalculating a reference signal based on said received block, if saidblock of said received multicarrier signal is not said training block;means for iteratively generating an estimation of channelcharacteristics using said reference signal, if said block of saidreceived multicarrier signal is not said training block; means foriteratively incrementing the block number; means for iterativelydetermining if the end of said frame has been reached; and means foriteratively accepting a next block of received multicarrier signal ifsaid end of said frame has not been reached.
 3. The system according toclaim 2, wherein said means for decoding is performed using${\hat{c}}_{n} = {\underset{c_{n}}{\arg \quad \min}{\sum\limits_{m}{{{x_{m,n} - {{\hat{H}}_{m,n}c_{n}}}}^{2}.}}}$


4. The system according to claim 2, wherein said means for calculatingis performed using${\overset{\sim}{H}}_{m,n} = {\underset{H_{m,n}}{\arg \quad \min}{\sum\limits_{m}{{{x_{m,n} - {H_{m,n}{\hat{c}}_{n}}}}^{2}.}}}$


5. The system according to claim 2, wherein said first means forgenerating is performed using${{\sum\limits_{l = 1}^{M_{L}}{B_{l}{d\left( {\overset{\sim}{H}}_{m,{n + 1 - l}} \right)}}} - {d\left( {\hat{H}}_{m,n} \right)}} = 0$


6. The system according to claim 2, wherein said second means forgenerating is performed using${{\sum\limits_{l = 1}^{M_{L}}{B_{l}{d\left( {\overset{\sim}{H}}_{m,{n + 1 - l}} \right)}}} - {d\left( {\hat{H}}_{m,{n + 1}} \right)}} = 0.$


7. The system according to claim 1, further comprising means fordeinterleaving said combined signal if said combined signal wasinterleaved for transmission.
 8. The system according to claim 1,wherein said means for demodulating is performed concurrently for allsignals of said received multicarrier signal.
 9. The system according toclaim 2, wherein said first means for generating is performed using${{\sum\limits_{l = 1}^{M_{1}}{b_{l}^{T}{\overset{\sim}{H}}_{m,{n + 1 - l}}}} - {\hat{H}}_{m,n}} = 0.$


10. The system according to claim 2, wherein said second means forgenerating is performed using${{\sum\limits_{l = 1}^{M_{1}}{b_{l}^{T}{\overset{\sim}{H}}_{m,{n + 1 - l}}}} - {\hat{H}}_{m,{n + 1}}} = 0.$


11. The system according to claim 1, wherein Fast Fouriertransformations are applied to each carrier of said receivedmulticarrier signal.
 12. The system according to claim 1, wherein saidmeans for demodulating is performed using QPSK techniques.
 13. A systemfor estimating channel characteristics in a multicarrier transmissionsystem comprising: means for iteratively receiving a multicarriersignal; means for iteratively applying Fast Fourier transformations tocarriers of said multicarrier signal; means for iteratively estimatingchannel characteristics of a multicarrier channel over which saidreceived multicarrier signal was transmitted using iterative backwardprocessing, wherein said means for iterative backward processing furthercomprises: means for iteratively determining if a block in a frame inthe received multicarrier signal is correct; means for iterativelydecoding said block of said received signal tentatively, if said blockof said received multicarrier signal is correct; means for iterativelycalculating a tentative reference signal based on said training block,if said block of said received multicarrier signal is correct; means foriteratively generating a tentative estimation of channel characteristicsusing said tentative reference signal, if said block of said receivedmulticarrier signal is correct; means for iteratively decoding saidblock of said received multicarrier signal, if said block of saidreceived multicarrier signal is not correct; means for iterativelycalculating a reference signal based on said received block, if saidblock of said received multicarrier signal is correct; means foriteratively generating an estimation of channel characteristics usingsaid reference signal, if said block of said received multicarriersignal is not correct; means for iteratively decoding said transformedreceived multicarrier signal; means for iteratively decrementing blocknumber; means for iteratively determining if a beginning of said framehas been reached; and means for iteratively accepting a next block ofreceived signal if said beginning of said frame has not been reached.14. The system according to claim 13, wherein said means for decoding isperformed using${\hat{c}}_{n} = {\underset{c_{n}}{\arg \quad \min}{\sum\limits_{m}{{{x_{m,n} - {{\hat{H}}_{m,n}c_{n}}}}^{2}.}}}$


15. The system according to claim 13, wherein said means for calculatingis performed using${\overset{\sim}{H}}_{m,n} = {\underset{H_{m,n}}{\arg \quad \min}{\sum\limits_{m}{{{x_{m,n} - {H_{m,n}{\hat{c}}_{n}}}}^{2}.}}}$


16. The system according to claim 13, wherein said first means forgenerating is performed using${{\sum\limits_{l = 1}^{M_{L}}{B_{l}{d\left( {\overset{\sim}{H}}_{m,{n + l - 1}} \right)}}} - {d\left( {\hat{H}}_{m,n} \right)}} = 0$


17. The system according to claim 13, wherein said second means forgenerating is performed using${{\sum\limits_{l = 1}^{M_{L}}{B_{l}{d\left( {\overset{\sim}{H}}_{m,{n + l - 1}} \right)}}} - {d\left( {\hat{H}}_{m,{n - 1}} \right)}} = 0.$


18. The system according to claim 13, wherein said means for decodingfurther comprises: means for iteratively demodulating said multicarrierreceived signal; means for iteratively combining said demodulatedreceived multicarrier signal using a maximum ratio combiner; and meansfor iteratively Viterbi decoding said combined signal.
 19. The systemaccording to claim 21, further comprising means for deinterleaving saidcombined signal if said combined signal was interleaved fortransmission.
 20. The system according to claim 18, wherein said meansfor demodulating is performed concurrently for all signals of saidmulticarrier signal.
 21. The system according to claim 21, wherein saidmeans for demodulating is performed concurrently for all signals of saidreceived multicarrier signal.
 22. The system according to claim 13,wherein said means for generating is performed using${{\sum\limits_{l = 1}^{M_{L}}{B_{l}^{T}{\overset{\sim}{H}}_{m,{n + l}}}} - {\hat{H}}_{m,n}} = 0.$


23. The system according to claim 13, wherein Fast Fouriertransformations are applied to each carrier of said multicarrier signal.24. The system according to claim 18, wherein said means fordemodulating is performed using QPSK techniques.
 25. A system forestimating channel characteristics in a multicarrier transmission systemcomprising: means for receiving a multicarrier signal; means forapplying Fast Fourier transformations to carriers of said receivedmulticarrier signal; means for estimating channel characteristics of amulticarrier channel over which said received multicarrier signal wastransmitted concurrently using iterative processing and iterativebackward processing; and means for decoding said transformedmulticarrier signal.
 26. The system according to claim 25, wherein saidmeans for iterative forward processing further comprises: means foriteratively determining if a block in a frame in the received signal isa training block; means for iteratively decoding said block of saidreceived multicarrier signal tentatively, if said block of said receivedmulticarrier signal is said training block; means for iterativelycalculating a tentative reference signal based on said training block,if said block of said received multicarrier signal is said trainingblock; means for iteratively generating a tentative estimation ofchannel characteristics using said tentative reference signal, if saidblock of said received multicarrier signal is said training block; meansfor iteratively decoding said block of said received multicarriersignal, if said block of said received multicarrier signal is not saidtraining block; means for iteratively calculating a reference signalbased on said received block, if said block of said receivedmulticarrier signal is not said training block; means for iterativelygenerating an estimation of channel characteristics using said referencesignal, if said block of said received multicarrier signal is not saidtraining block; means for iteratively incrementing block number; meansfor iteratively determining if the end of said frame has been reached;and means for iteratively accepting a next block of received signal ifsaid end of said frame has not been reached.
 27. The system according toclaim 25, wherein said means for iterative backward processingcomprises: means for iteratively determining if a block in a frame inthe received multicarrier signal is correct; means for iterativelydecoding said block of said received signal tentatively, if said blockof said received multicarrier signal is correct; means for iterativelycalculating a tentative reference signal based on said block, if saidblock of said received multicarrier signal is correct; means foriteratively generating a tentative estimation of channel characteristicsusing said tentative reference signal, if said block of said receivedmulticarrier signal is correct; means for iteratively decoding saidblock of said received multicarrier signal, if said block of saidreceived multicarrier signal is not correct; means for iterativelycalculating a reference signal based on said received block, if saidblock of said received multicarrier signal is not correct; means foriteratively generating an estimation of channel characteristics usingsaid reference signal, if said block of said received multicarriersignal is not correct; means for iteratively decrementing the blocknumber; means for iteratively determining if the beginning of said framehas been reached; and means for iteratively accepting a next block ofreceived signal if said beginning of said frame has not been reached.28. The system according to claim 25, wherein said means for decodingfurther comprises: means for demodulating said multicarrier receivedsignal; means for combining said demodulated multicarrier signal using amaximum ratio combiner; and means for Viterbi decoding said combinedsignal.
 29. The system according to claim 28, further comprising themeans for deinterleaving said combined signal if said combined signalwas interleaved for transmission.
 30. The system according to claim 28,wherein said means for demodulating is performed using QPSK techniques.31. The system according to claim 25, wherein Fast Fouriertransformations are applied to each carrier of said receivedmulticarrier signal.
 32. A system for estimating channel characteristicsin a multicarrier transmission system comprising: a plurality of FastFourier transformers for transforming a received multicarrier signalfrom a frequency domain to a time domain; a channel estimator connectedto said plurality of said Fast Fourier transformers; a plurality ofdemodulators configured for receiving a signal which is a sum of outputsof said Fast Fourier transformers and said channel estimator; a maximumratio combiner connected to said plurality of said demodulators; and aViterbi decoder connected to said maximum ratio combiner, said Viterbidecoder further connected to said channel estimator to provide feedbackinput to said channel estimator.
 33. The system according to claim 32,wherein a deinterleaver is interposed between said maximum ratiocombiner and said Viterbi decoder, if an interleaver was used intransmitting said received multicarrier signal.
 34. The system accordingto claim 32, wherein said demodulators are QPSK.